Easter Term 2013

Tuesday 30th April, 14:30-15:30
LARGE Seminar Room, 1st Floor, Institute of Public Health

Rhian Daniel
London School of Hygiene and Tropical Medicine

“Causation mediation analysis with multiple markers”

Abstract: Epidemiologic analyses often attempt to decompose the effect of an exposure on an outcome into its effect via a number of different pathways. For example, the effect of heavy alcohol consumption on systolic blood pressure (SBP) may be separated into an effect via body mass index (BMI), an effect via the liver enzyme gamma-glutamyl transpeptidase (GGT), an effect via both BMI and GGT, and an effect via other pathways (not through BMI or GGT )–often called the direct effect. Much progress has been made, mainly due to contributions from the field of causal inference, in understanding the precise nature of estimands that capture these sorts of effects, the assumptions under which they can be identified from data, and statistical estimation methods for doing so. However, the focus in the causal inference literature has been mostly on the decomposition of an effect around and through a single mediator, or a set of mediators considered en bloc, hence the two components: a direct and an indirect effect.
In this talk we describe novel, counterfactually-defined path-specific effects that permit the decomposition of the total effect of an exposure on an outcome into a sum of numerous path-specific effects through many mediators, where the mediators are permitted to have a causal effect on each other. We show that there are many ways in which this decomposition can be done, discuss the strong structural and modelling assumptions under which the effects can be estimated, together with a sensitivity analysis approach when a particular subset of the assumptions cannot be justified. Illustrating these ideas using data on alcohol consumption, SBP , BMI and GGT from the Izhevsk Family Study, we focus on the ambitious nature of multiple mediation analyses, giving some practical guidance on how progress can be made.

Tuesday 14th May, 14:30-15:30
LARGE Seminar Room, 1st Floor, Institute of Public Health

Andrew Titman
University of Lancaster

“Semi Markov models under panel observation”

Abstract:Multi-state models are widely used in event history analysis. Often the state of the process is only known at a set of discrete, potentially unequally spaced and subject specific, examination times leading to panel data. Most analyses for panel data assume a Markov model, but we may instead wish to allow the transition intensities to depend on the time spent in the current state leading to a semi-Markov model. The likelihood for general semi-Markov models is somewhat intractable. This talk focuses on semi-Markov models with phase-type sojourn distributions which allow an aggregated (or hidden) Markov representation making computation simpler. Two main approaches can be considered. Firstly, the states in the model can be assumed to have phase-type distributions directly [1]. Alternatively, phase-type distributions approximations to parametric distributions can be used to build an approximate likelihood for Weibull or Gamma semi-Markov models [2]. In either case, the addition of misclassification of the disease states can be incorporated relatively easily. The methods are illustrated on chronic disease data from post-lung-transplantation patients.
[1] Titman A.C., Sharples L.D. Semi-Markov models with phase-type sojourn distributions. Biometrics. 2010. 66 (3): 742-752.
[2] Titman A.C. Estimating parametric semi-Markov models from panel data using phase-type approximations. Statistics and Computing. 2012. Online First

Tuesday 4th June, 14:30-15:30
LARGE Seminar Room, 1st Floor, Institute of Public Health

Trevor Lewis
Royal Statistical Society

“The value of CStat”

Tuesday 18th June, 14:30-15:30
LARGE Seminar Room, 1st Floor, Institute of Public Health

Sue Todd
University of Reading

Title:TBC